Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.
{"title":"Random forest and support vector machine classifiers for coastal wetland characterization using the combination of features derived from optical data and synthetic aperture radar dataset","authors":"Sandra Maria Cherian, Rajitha K","doi":"10.2166/wcc.2023.238","DOIUrl":"https://doi.org/10.2166/wcc.2023.238","url":null,"abstract":"\u0000 \u0000 Mapping mangrove forests is crucial for their conservation, but it is challenging due to their complex characteristics. Many studies have explored machine learning techniques that use Synthetic Aperture Radar (SAR) and optical data to improve wetland classification. This research compares the random forest (RF) and support vector machine (SVM) algorithms, employing Sentinel-1 dual polarimetric C-band data and Sentinel-2 optical data for mapping mangrove forests. The study also incorporates various derived parameters. The Jeffries–Matusita distance and Spearman’s rank correlation are used to evaluate the significance of commonly used spectral indices and SAR parameters in wetland classification. Only significant parameters are retained, reducing data dimensionality from 63 initial features to 23–33 essential features, resulting in an 18% improvement in classification accuracy. The combination of SAR and optical data yields a substantial 33% increase in the overall accuracy for both SVM and RF classification. Consistently, the fusion of SAR and optical data produces higher classification accuracy in both RF and SVM algorithms. This research provides an effective approach for monitoring changes in Pichavaram wetlands and offers a valuable framework for future wetland monitoring, supporting the planning and sustainable management of this critical area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139010766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chennai is a rapidly urbanizing Indian megacity and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year making it essential to study the historical trend and forecast changes in LULC and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff of 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%, and climate analysis shows a 74% probability of an increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river, which flows through Chennai city. The canals were found to prevent overbanking, thereby providing complete protection against flooding. It is proved that this is the best possible measure that provides the highest flood reduction for the study area.
{"title":"Modelling and forecasting of urban flood under changing climate and land use land cover","authors":"S. Anuthaman, Saravanan R., Balamurugan R., B. L.","doi":"10.2166/wcc.2023.164","DOIUrl":"https://doi.org/10.2166/wcc.2023.164","url":null,"abstract":"\u0000 Chennai is a rapidly urbanizing Indian megacity and experiences flooding frequently. Literature state that climate change and land use change have a significant impact on the runoff generated every year making it essential to study the historical trend and forecast changes in LULC and climate to model runoff. This study considered Adyar watershed for LULC change detection, climate change analysis, and flood forecasting for 2030 and 2050 based on LULC and runoff of 2005 and 2015. A coupled hydrologic–hydraulic model (HEC-HMS and HEC-RAS) was developed to assess flooding for future LULC and climate scenarios. LULC analysis shows an increase in built-up cover by 6%, and climate analysis shows a 74% probability of an increase in precipitation intensity between 2015 and 2050 compared to 2015. It was observed that depth of flooding increased by 19.4% in 2030 and 60.4% in 2050 compared to 2015. This study makes a structural proposition for flood mitigation through flood carrier canals on the downstream reach of the river, which flows through Chennai city. The canals were found to prevent overbanking, thereby providing complete protection against flooding. It is proved that this is the best possible measure that provides the highest flood reduction for the study area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138597079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sinuhé Sánchez, Fernando J. González Villarreal, Ramón Domínguez Mora, M. L. Arganis Juárez
The aim of this study was to investigate the existence and the magnitude of trend in different areas and durations of TCR. To achieve this objective, a mixed-method approach was employed using depth–area–duration and areal reduction factor (ARFs) curves that can be described as a logarithm equation to generate time series that allows the application of statistical methods such as the Mann–Kendall (MK) and Spearman Rho (SR) to detect trends. Time series are generated by substituting different areas in the logarithmic equations. The evidence presented shows that in Mexico, the TCR lasting 24 h shows an increasing trend for maximum areas between 300 and 1,700 km2 according to the MK and SR tests, respectively; according to these same tests for durations of 48 h, upward trends were observed up to maximum areas between 5,700 and 6,900 km2. The Sen slope reports annual increases between 0.76 and 1.32 mm and between 1.15 and 2.06 for a duration of 24 and 48 h, respectively. In contrast, no trends were observed in the time series obtained from the ARFs. Finally, the Pettitt test reports an abrupt jump from the year 1997 in all cases.
{"title":"Trend in rainfall associated with tropical cyclones in Mexico attributed to climate change and variability","authors":"Sinuhé Sánchez, Fernando J. González Villarreal, Ramón Domínguez Mora, M. L. Arganis Juárez","doi":"10.2166/wcc.2023.300","DOIUrl":"https://doi.org/10.2166/wcc.2023.300","url":null,"abstract":"\u0000 \u0000 The aim of this study was to investigate the existence and the magnitude of trend in different areas and durations of TCR. To achieve this objective, a mixed-method approach was employed using depth–area–duration and areal reduction factor (ARFs) curves that can be described as a logarithm equation to generate time series that allows the application of statistical methods such as the Mann–Kendall (MK) and Spearman Rho (SR) to detect trends. Time series are generated by substituting different areas in the logarithmic equations. The evidence presented shows that in Mexico, the TCR lasting 24 h shows an increasing trend for maximum areas between 300 and 1,700 km2 according to the MK and SR tests, respectively; according to these same tests for durations of 48 h, upward trends were observed up to maximum areas between 5,700 and 6,900 km2. The Sen slope reports annual increases between 0.76 and 1.32 mm and between 1.15 and 2.06 for a duration of 24 and 48 h, respectively. In contrast, no trends were observed in the time series obtained from the ARFs. Finally, the Pettitt test reports an abrupt jump from the year 1997 in all cases.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
T. V. Tra, Van Thi Hang, Ngo Thi Thuy, Dang Thi Lan Phuong, Phan Van Thanh
Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.
{"title":"Using a scenario-neutral approach to assess the impacts of climate change on flooding in the Ba River Basin, Viet Nam","authors":"T. V. Tra, Van Thi Hang, Ngo Thi Thuy, Dang Thi Lan Phuong, Phan Van Thanh","doi":"10.2166/wcc.2023.569","DOIUrl":"https://doi.org/10.2166/wcc.2023.569","url":null,"abstract":"\u0000 \u0000 Due to the hydrologic non-stationarity and uncertainty related to the probability assignment of flood peaks under climate change, the use of flood statistics may no longer be applicable. Therefore, a sensitivity analysis (i.e., a scenario-neutral approach) is used to examine the impacts of climate change on flooding in the Ba River Basin. A Delphi method with a set of KAMET rules was used to obtain a representative and a threshold flood event. These inputs are used for hydraulic simulation using a MIKE FLOOD model package. Flood simulations were performed using parametrically varied rainfall and temperature conditions. In total, 22 conditions were explored and are in line with CMIP5 and CMIP6. The results obtained have several implications. Firstly, rainfall change is the primary factor affecting flood impact in the Ba River Basin. Secondly, the flood peak in the Ba River Basin is highly sensitive to an increase in rainfall by up to 10%. Thirdly, the flooded threshold is reached when rainfall increases beyond 20%. Fourthly, the flood extent and depth are expected to increase as rainfall increases. Further research could improve the study using satellite rainfall data, satellite digital elevation models, and stochastic weather generators.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138601253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The unpredictability of crop yield due to severe weather events such as drought and extreme heat continue to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (multilayer perception (MLP)), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.
{"title":"Early crop yield prediction for agricultural drought monitoring using drought indices, remote sensing, and machine learning techniques","authors":"Parthsarthi Pandya, Narendra Kumar Gontia","doi":"10.2166/wcc.2023.386","DOIUrl":"https://doi.org/10.2166/wcc.2023.386","url":null,"abstract":"\u0000 \u0000 The unpredictability of crop yield due to severe weather events such as drought and extreme heat continue to be a key worry. The present study evaluated six meteorological and three Landsat satellite-based vegetation drought indices from 1986 to 2019 in the drought-prone-semi-arid Saurashtra region of Gujarat (India). Cotton and groundnut crop yield prediction models were developed using multiple linear regression (multilayer perception (MLP)), artificial neural network with MLP, and random forest (RF). The models performed crop yield estimation at two timescales, i.e., 75 days after sowing and 105 days after sowing. The standardized precipitation evapotranspiration index/reconnaissance drought index among meteorological drought indices, normalized difference vegetation anomaly index/vegetation condition index, and normalized difference water index anomaly were chosen as best highest correlations with crop yields. The RF-based models were found most efficient in predicting the cotton and groundnut yield of Saurashtra with R2 ranging from 0.77 to 0.92, Nash–Sutcliffe efficiency ranging from 71 to 90%, and root-mean-square error ranging from 80 to 133 kg/ha for cotton and 299 to 453 kg/ha for groundnut. This study demonstrated the method for making several decisions based on early crop yield prediction including timely drought mitigation measures.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138611502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study presents a sediment rating curve (SRC), multiple regression (MR), and long short-term memory (LSTM) model for estimating daily suspended sediment concentration (SSC). The data of daily SSC at Yen Thuong and daily flow at five locations in the Ca River Basin, Vietnam are used to demonstrate multiple approaches. Using the daily flow and SSC data in the period from 2009 to 2019, appropriate coefficients in each method are identified carefully using five popular criteria. The results showed that SRC and MR approaches reproduced acceptably the observed values, with the values of RMSE, MAE, and ME of daily SSC being less than 5% of daily SSC magnitude observed at the station, while NSE ranges from 0.47 to 0.63 and r coefficient varies between 0.69 and 0.80. The LSTM model represented the observed values of daily SSC very well. The values of two dimensionless criteria are greater than 0.94 and its values of three-dimensional criteria are smaller than 2.0% of the observed magnitude of daily SSC in both training and validation steps. The LSTM model is found to be the best among the three investigated approaches. Then, the model is applied to estimate daily SSC values for the period from 1969 to 2008 and the year 2020.
{"title":"Estimation of daily suspended sediment concentration in the Ca River Basin using a sediment rating curve, multiple regression, and long short-term memory model","authors":"Chien Pham Van, Hien T. T. Le, Le Van Chin","doi":"10.2166/wcc.2023.229","DOIUrl":"https://doi.org/10.2166/wcc.2023.229","url":null,"abstract":"\u0000 \u0000 This study presents a sediment rating curve (SRC), multiple regression (MR), and long short-term memory (LSTM) model for estimating daily suspended sediment concentration (SSC). The data of daily SSC at Yen Thuong and daily flow at five locations in the Ca River Basin, Vietnam are used to demonstrate multiple approaches. Using the daily flow and SSC data in the period from 2009 to 2019, appropriate coefficients in each method are identified carefully using five popular criteria. The results showed that SRC and MR approaches reproduced acceptably the observed values, with the values of RMSE, MAE, and ME of daily SSC being less than 5% of daily SSC magnitude observed at the station, while NSE ranges from 0.47 to 0.63 and r coefficient varies between 0.69 and 0.80. The LSTM model represented the observed values of daily SSC very well. The values of two dimensionless criteria are greater than 0.94 and its values of three-dimensional criteria are smaller than 2.0% of the observed magnitude of daily SSC in both training and validation steps. The LSTM model is found to be the best among the three investigated approaches. Then, the model is applied to estimate daily SSC values for the period from 1969 to 2008 and the year 2020.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138617184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In this research, the impact of climate change in the next 15 years (2036–2022) in the (LarDam) area has been investigated. The results showed that in the case of climate change under scenarios RCP2.6, RCP4.5, RCP8.5, the maximum temperature and the minimum temperature have increased by5, 5.23, 6.2% and 3.5, 5.6, 5.17%, respectively, and the amount of precipitation increased by 8.55, 9.5, 13%, respectively. Also, the highest rainfall will be in 2031 and the lowest will be in 2036. Then, based on the intermediate state of the scenarios, i.e. RCP4.5 scenario, the amount of runoff was obtained and the reliability index was calculated according to the upstream runoff of Lar Dam and downstream needs for drinking, agriculture, and environment. The simulation was also performed in the WEAP model. The obtained reliability showed that the highest reliability was 86.60% of the agriculture needs in the WEAP model, and by using the optimization of a honey badger and harmonic search algorithms, it was found that the reliability is approximately 5.06 and 1.73% higher than the reliability of the simulation, respectively. Moreover, in comparison with the optimization algorithms, due to the smaller value of the objective function of the honey badger algorithm and the greater reliability of this algorithm in optimizing downstream needs, it can be concluded that the performance of this algorithm was better than the harmonic search algorithm. The honey badger algorithm has a faster calculation speed than the harmony search algorithm with less execution time.
{"title":"Simulation and optimization of Lar Dam reservoir storage under climate change conditions","authors":"Hediyeh Sadeghijou, Amirpouya Sarraf, Hassan Ahmadi","doi":"10.2166/wcc.2023.225","DOIUrl":"https://doi.org/10.2166/wcc.2023.225","url":null,"abstract":"Abstract In this research, the impact of climate change in the next 15 years (2036–2022) in the (LarDam) area has been investigated. The results showed that in the case of climate change under scenarios RCP2.6, RCP4.5, RCP8.5, the maximum temperature and the minimum temperature have increased by5, 5.23, 6.2% and 3.5, 5.6, 5.17%, respectively, and the amount of precipitation increased by 8.55, 9.5, 13%, respectively. Also, the highest rainfall will be in 2031 and the lowest will be in 2036. Then, based on the intermediate state of the scenarios, i.e. RCP4.5 scenario, the amount of runoff was obtained and the reliability index was calculated according to the upstream runoff of Lar Dam and downstream needs for drinking, agriculture, and environment. The simulation was also performed in the WEAP model. The obtained reliability showed that the highest reliability was 86.60% of the agriculture needs in the WEAP model, and by using the optimization of a honey badger and harmonic search algorithms, it was found that the reliability is approximately 5.06 and 1.73% higher than the reliability of the simulation, respectively. Moreover, in comparison with the optimization algorithms, due to the smaller value of the objective function of the honey badger algorithm and the greater reliability of this algorithm in optimizing downstream needs, it can be concluded that the performance of this algorithm was better than the harmonic search algorithm. The honey badger algorithm has a faster calculation speed than the harmony search algorithm with less execution time.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134991279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rihan Al Saodi, Mustafa Al Kuisi, Ahmed Al Salaymeh
Abstract The objective of this study was to evaluate the sensitivity of flash floods to future climate change in the Amman–Zarqa Basin, Jordan. Historical daily rainfall and temperature data from 1970 to 2018 were collected, along with projected daily data derived from general circulation models (GCMs) forecast spanning 2019–2060. The methodology involved analyzing historical and model forecast data, conducting trend analysis, mapping changes in land use, estimating runoff volume, selecting indicators, assigning their weights through the analytical hierarchy process, and generating vulnerability maps. Analysis of precipitation trends revealed a 14.61% decrease in total annual rainfall over the past 48 years; however, future projections indicate a 5.26% increase. Downstream sub-catchments in the arid portion are projected to receive higher rainfall, while upstream sub-catchments are expected to experience a substantial decline, resulting in an overall reduction in runoff. Moreover, our findings demonstrate a rising trend in mean temperature, which is expected to persist. Remote sensing data indicate a 14.76% expansion of urban areas, indicative of rapid population growth. Although no highly vulnerable sub-catchments were identified, downstream sub-catchments 8 and 9 exhibited moderate vulnerability to flash floods, which can be attributed to the increase in rainfall and insufficient stormwater infrastructure.
{"title":"Assessing the vulnerability of flash floods to climate change in arid zones: Amman–Zarqa Basin, Jordan","authors":"Rihan Al Saodi, Mustafa Al Kuisi, Ahmed Al Salaymeh","doi":"10.2166/wcc.2023.237","DOIUrl":"https://doi.org/10.2166/wcc.2023.237","url":null,"abstract":"Abstract The objective of this study was to evaluate the sensitivity of flash floods to future climate change in the Amman–Zarqa Basin, Jordan. Historical daily rainfall and temperature data from 1970 to 2018 were collected, along with projected daily data derived from general circulation models (GCMs) forecast spanning 2019–2060. The methodology involved analyzing historical and model forecast data, conducting trend analysis, mapping changes in land use, estimating runoff volume, selecting indicators, assigning their weights through the analytical hierarchy process, and generating vulnerability maps. Analysis of precipitation trends revealed a 14.61% decrease in total annual rainfall over the past 48 years; however, future projections indicate a 5.26% increase. Downstream sub-catchments in the arid portion are projected to receive higher rainfall, while upstream sub-catchments are expected to experience a substantial decline, resulting in an overall reduction in runoff. Moreover, our findings demonstrate a rising trend in mean temperature, which is expected to persist. Remote sensing data indicate a 14.76% expansion of urban areas, indicative of rapid population growth. Although no highly vulnerable sub-catchments were identified, downstream sub-catchments 8 and 9 exhibited moderate vulnerability to flash floods, which can be attributed to the increase in rainfall and insufficient stormwater infrastructure.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135041806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mamush Tekle Assfaw, Bogale Gebremariam Neka, Elias Gebeyehu Ayele
Abstract In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario.
{"title":"Modeling the impact of climate change on streamflow responses in the Kessem watershed, Middle Awash sub-basin, Ethiopia","authors":"Mamush Tekle Assfaw, Bogale Gebremariam Neka, Elias Gebeyehu Ayele","doi":"10.2166/wcc.2023.541","DOIUrl":"https://doi.org/10.2166/wcc.2023.541","url":null,"abstract":"Abstract In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135185921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This work investigates the meteorological mechanisms forming a classical frontal system on 26 August 2020 in the northeast and eastern parts of Afghanistan. The weather system caused heavy rainfall and led to severe flash floods. Flooding, affected by torrential rain showers, struck mostly the city of Charikar in Parvan province early in the morning day, while most people were asleep. This caused 150 deaths, and nearly 500 houses were destroyed. This research explores atmospheric processes by examining the National Centers for Environmental Prediction dataset and MERRA Model database. The calculation of the convective available potential energy (CAPE) and Showalter index extracted from the Skew-T log-pressure diagram shows a high value of the CAPE at around 2,632 J/kg and −6.6 for the Showalter index, respectively. This presents a very extreme instability in the study area during the time of the flood. The study reveals that the triggering of this system was mostly by thermodynamical aspects, low-level deep convergence, and local topographical aspects rather than the PV streamer. However, the anomaly climate analysis for different atmospheric elements with a comparison of the climate normal values shows the importance of climate change in the weather system into a stronger frontal activity associated with stronger baroclinicity over the study area.
{"title":"A case study of an extreme flooding episode in Charikar, Eastern Afghanistan","authors":"Farahnaz Fazel-Rastgar, Venkataraman Sivakumar","doi":"10.2166/wcc.2023.462","DOIUrl":"https://doi.org/10.2166/wcc.2023.462","url":null,"abstract":"Abstract This work investigates the meteorological mechanisms forming a classical frontal system on 26 August 2020 in the northeast and eastern parts of Afghanistan. The weather system caused heavy rainfall and led to severe flash floods. Flooding, affected by torrential rain showers, struck mostly the city of Charikar in Parvan province early in the morning day, while most people were asleep. This caused 150 deaths, and nearly 500 houses were destroyed. This research explores atmospheric processes by examining the National Centers for Environmental Prediction dataset and MERRA Model database. The calculation of the convective available potential energy (CAPE) and Showalter index extracted from the Skew-T log-pressure diagram shows a high value of the CAPE at around 2,632 J/kg and −6.6 for the Showalter index, respectively. This presents a very extreme instability in the study area during the time of the flood. The study reveals that the triggering of this system was mostly by thermodynamical aspects, low-level deep convergence, and local topographical aspects rather than the PV streamer. However, the anomaly climate analysis for different atmospheric elements with a comparison of the climate normal values shows the importance of climate change in the weather system into a stronger frontal activity associated with stronger baroclinicity over the study area.","PeriodicalId":49150,"journal":{"name":"Journal of Water and Climate Change","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135139455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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